Deriving DNN architecture can be modeled as a sequential decision making problem and RL is a suitable approach for solving that. In [1]}, a meta-modeling algorithm based on RL named MetaQNN is introduced to generate CNN architecture. The process of CNN architecture selection is automated by a Q-learning agent whose goal is to find the best CNN architecture for a particular machine learning task. The validation accuracy of the given ML dataset is used as the reward value for the agent and the actions are obtained by following \(\epsilon \) -greedy algorithm and exploring in a discrete and finite space of layer parameters. This approach shows high performance for image classification tasks.
